| Summary: | The potential for wind power development in Greenland is evaluated based on the analysis of 58 years of data (1957-2015) from RACMO 2.3 (Regional Atmospheric Climate Model). In order to create a wind power development tool, mesoscale maps based on RACMO 2.3 model were created containing the following characteristics: mean wind speeds (at 10 m), averaged maximum wind speed (with and without gusts at 10 m), temperature, humidity, geopotential, ice sheet mask and land sheet mask. A relevant aspect for this thesis is the mean wind speed. Over Greenland, the lower mean wind speeds range from 2-3 m/s on the tundra areas near the coast. This is influenced by high temperature inversion over the arctic tundra which disintegrates the predominant katabatic flow leading to lower wind speeds. On the other hand, the highest mean wind speeds range from 6 to 10 m/s and are observed in the northeastern region, due to cyclonic activity over the Greenland Sea. Maps of both the mean wind speed and averaged maximum wind speed are combined in order to achieve the highest mean wind speed value while at the same time avoiding maximum wind speeds higher than the cut-off value of the selected turbine model. This map combination is synchronized with pre-determined construction constraints, resulting in the suggestion of three different sites (sites 4, 5 and 6) as potential targets for wind power development. Multi-level data is sorted for different heights (10, 35, 70, 100 and 120 m) to perform a micro-scale analysis exercise for the three different site suggestions. A Vestas V90 3MW with an 80 meter hub height is selected as the standard turbine model to be deployed at the three recommended positions and for use in further simulations using WindSim. Annual Energy Production (AEP) for these three turbines in the recommended locations is calculated based on the interpolation from the climatology data at 70 m which is closest to the turbines’ hub heights. The AEP results are compared and show that site suggestion 4 presents the best ...
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